basic parsing working

2025-12-11 12:56:23 +01:00
parent b023d44934
commit e576f98cce
10 changed files with 411 additions and 183 deletions
--- a/utils/sentiment_analysis.py
+++ b/utils/sentiment_analysis.py
@@ -0,0 +1,128 @@
+import random
+import pandas as pd
+
+from ollama import Client
+import json
+
+def dummy_sentiment_analysis(content, tag):
+    if tag.startswith('VT -') or tag.startswith('CT -'):
+        return random.choice([-1, 0, 1]), 'random dummy sentiment'  # Random sentiment for testing
+
+    return 'test', 'not applicable'
+
+
+
+def ollama_sentiment_analysis(content, theme, client: Client, model) -> tuple[list[str], int, str]:
+    """
+    Perform sentiment analysis using Ollama model.
+
+    Parameters:
+    - content: Text content to analyze
+    - tag: Tag indicating the type of sentiment analysis (e.g., 'VT - Positive')
+
+    Returns:
+    - sentiment score and reason
+    """
+    prompt = f"""
+    # Instructions
+    You are an expert in sentiment analysis and natural language processing. You are given a quote from an interview along with a theme tag. Your task is to analyze the sentiment expressed in the quote in relation to the provided theme, and provide a short explanation for your assessment (max 10 words).
+    
+    You need to deliver three pieces of information:
+    1. A list of keywords from the quote quantify or qualify the theme, and that influenced your sentiment analysis (if any).
+    2. A sentiment score: -1 for negative, 0 for neutral, and 1 for positive sentiment.
+    3. A brief reason (max 10 words) explaining your sentiment score.
+    
+
+    # Guidelines    
+    Keywords should be directly relevant to the theme.
+    
+    The reason should be extremely concise and to the point:
+    - Does not need to be a full sentence.
+    - Sentiment itself does not need to be stated in the explanation.
+    - If keywords are present in the quote that directly capture the sentiment, give that as the reason..
+    
+    
+    # Input
+    
+    Theme: `{theme}`
+    
+    Quote:
+    ```
+    {content}
+    ```
+    
+    # Response Format
+    Provide your response in the following JSON format:
+    {{
+        "keywords": ["<list_of_relevant_keywords_if_any>"],
+        "sentiment": <sentiment_score>,
+        "reason": "<brief_explanation_max_10_words>"
+    }}
+
+    
+    # Examples
+    
+    ** Example 1**
+    - Theme: `Speed`
+    - Quote: `It just was a little toned down. It was almost like he was talking like this. You know? It almost kind of this was a little slow for me.`
+
+    - Response: {{"keywords": ["slow"], "sentiment": -1, "reason": "States speed is slow, indicates dissatisfaction"}}
+    
+    ** Example 2**
+    - Theme: `Friendliness / Empathy`
+    - Quote: `Sound very welcoming`
+    
+    - Response: {{ "keywords": ["welcoming"], "sentiment": 1, "reason": "Uses 'welcoming'" }}
+    
+    """
+
+    resp = client.generate(
+        model=model,
+        prompt=prompt,
+    )
+    
+    try:
+        response_text = resp.response.strip()
+
+        # Extract JSON from response
+        start_index = response_text.find('{')
+        end_index = response_text.rfind('}') + 1
+        json_str = response_text[start_index:end_index]
+        
+        response_json = json.loads(json_str)
+        keywords = response_json.get('keywords', [])
+        sentiment = response_json.get('sentiment', 'test')
+        reason = response_json.get('reason', 'no reason provided')
+        return keywords, sentiment, reason
+    except Exception as e:
+        print(f"Error parsing response: {e}")
+        return [], None, 'parsing error'
+
+
+if __name__ == "__main__":
+
+    client = Client(
+            host="http://localhost:11434"
+        )
+
+    sentiment_df = pd.DataFrame({
+        'content': [
+            "I love this product!",
+            "This is the worst service ever.",
+            "It's okay, not great but not terrible."
+        ],
+        'tag': [
+            'VT - Personal Experience',
+            'VT - Personal Experience',
+            'VT - Personal Experience'
+        ],
+        'manual_analysis': [False, False, True]
+    })
+
+    sentiment_df[['sentiment', 'reason']] = sentiment_df[~sentiment_df['manual_analysis']].apply(
+        lambda row: pd.Series(ollama_sentiment_analysis(row['content'], row['tag'], client, model='llama3.2:latest')),
+        axis=1
+    )
+
+    print(sentiment_df.head())
+